Review of cognitive behavioural therapy mobile apps using a reference architecture embedded in the patient-provider relationship
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Mobile health apps (mHealth apps) are increasing in popularity and utility for the management of many chronic diseases. Although the current reimbursement structure for mHealth apps is lagging behind the rapidly improving functionality, more clinicians will begin to recommend these apps as they prove their clinical worth. Payors such as the government or private insurance companies will start to reimburse for the use of these technologies, especially if they add value to patients by providing timely support, a more streamlined patient experience, and greater patient convenience. Payors are likely to see benefits for providers, as these apps could help increase productivity between in-office encounters without having to resort to expensive in-person visits when patients are having trouble managing their disease. KEY FINDINGS: To guide and perhaps speed up adoption of mHealth apps by patients and providers, analysis and evaluation of existing apps needs to be carried out and more feedback must be provided to app developers. In this paper, an evaluation of 35 mHealth apps claiming to provide cognitive behavioural therapy was conducted to assess the quality of the patient-provider relationship and evidence-based practices embedded in these apps. The mean score across the apps was 4.9 out of 20 functional criteria all of which were identified as important to the patient-provider relationship. The median score was 5 out of these 20 functional criteria. CONCLUSION: Overall, the apps reviewed were mostly stand-alone apps that do not enhance the patient-provider relationship, improve patient accountability or help providers support patients more effectively between visits. Large improvements in patient experience and provider productivity can be made through enhanced integration of mHealth apps into the healthcare system.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it